--- title: The peak pattern puzzle keywords: fastai sidebar: home_sidebar summary: "Matching peak patterns " description: "Matching peak patterns " nb_path: "notebooks/50_peak-pattern-puzzle.ipynb" ---
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In the previous steps we have computed the hotmax spectra and within each spectrum detected peaks above the Poison noise level. All this preliminary work was needed to arrive at the central problem of MA-XRF analysis: solving the peak pattern puzzle. For each individual hotmax spectrum I would like to explain the presence of all peaks above the Poison noise level.

Before trying to assign peaks to the presence of specific elements in the scanned object, one should identify instrumental peaks that result from the physics of the instrument detector or Rhodium anode.

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from maxrf4u import HotmaxAtlas 

hma = HotmaxAtlas('RP-T-1898-A-3689.datastack')

hma.plot_spectra()
2022-08-30T09:42:08.143123 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/
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Now we can start to 'explain away' all peaks. For now, it is highly instructive to walk through all hotmax spectra and see which element patterns explain the peak patterns that we observe. To do so, import the plot_puzzle() and plot_ptrn() functions.

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from maxrf4u import plot_puzzle, plot_ptrn
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hma = HotmaxAtlas('RP-T-1898-A-3689.datastack')
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n = 0
ax0, ax1 = plot_puzzle(hma, n) 

# patterns 
plot_ptrn('Ca', -1, ax1);
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n = 1
ax0, ax1 = plot_puzzle(hma, n) 

# patterns 
plot_ptrn('O', -1, ax1);
plot_ptrn('Ca', -1, ax1);
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n = 2
ax0, ax1 = plot_puzzle(hma, n)


# patterns 
plot_ptrn('Pb', -1, ax1);
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n = 3
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Cl', -1, ax1);
plot_ptrn('Ca', -1, ax1);
plot_ptrn('Fe', -2, ax1);
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n = 4
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Fe', -3, ax1)
plot_ptrn('Ca', -1, ax1)
plot_ptrn('O', -1, ax1)
plot_ptrn('S', -1, ax1)
plot_ptrn('K', -2, ax1);
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n = 5
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Ca', -1, ax1);
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n = 6
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Ca', -1, ax1);
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n = 7
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Fe', -1, ax1);
plot_ptrn('Ti', -2, ax1);
plot_ptrn('Ca', -3, ax1);
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n = 8
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Fe', -3, ax1);
plot_ptrn('Ba', -1, ax1);
plot_ptrn('Ca', -2, ax1);
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n = 9
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Mn', -1, ax1);
plot_ptrn('Ca', -2, ax1);
plot_ptrn('Fe', -3, ax1);
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n = 10
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Fe', -1, ax1);
plot_ptrn('Ca', -2, ax1);
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The tiny peak [6] in hotmax spectrum #10 is clearly the silicon detector escape peak located at 6.40 keV minus 1.74 keV.

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n = 11
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Fe', -1, ax1);
plot_ptrn('Ca', -2, ax1);
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n = 12
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Cu', -1, ax1);
plot_ptrn('Zn', -2, ax1);
plot_ptrn('Ca', -3, ax1);
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n = 13
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Cu', -1, ax1);
plot_ptrn('Zn', -2, ax1);
plot_ptrn('Ca', -3, ax1);
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n = 14
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Cu', -1, ax1);
plot_ptrn('Zn', -2, ax1);
plot_ptrn('Ca', -3, ax1);
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n = 15
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Pb', -1, ax1);
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n = 16
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Pb', -1, ax1);
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n = 17
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Ca', -1, ax1);
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n = 18
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Pb', -1, ax1);
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n = 19
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Ca', -1, ax1);
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n = 20
ax0, ax1 = plot_puzzle(hma, n)

# patterns 
plot_ptrn('Pb', -1, ax1);
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Ok, that is it. Let's try to summarize what we have learned...

Summary

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from maxrf4u import plot_patterns, get_patterns, DataStack 
from maxrf4u.peakmaps import _add_hotlines_ticklabels

ds = DataStack('RP-T-1898-A-3689.datastack')

x_keVs = ds.read('maxrf_energies') 
y_max = ds.read('maxrf_maxspectrum')
y_sum = ds.read('maxrf_sumspectrum') 
hotmax_spectra = ds.read('hotmax_spectra')

ptrn_list = get_patterns(['S', 'Ca', 'K', 'Cl', 'Fe', 'Mn', 'Cu', 'Zn', 'Pb', 'Ti', 'Ba'])

fig, [ax, ax1] = plt.subplots(nrows=2, sharex=True, figsize=[9, 5])

plot_patterns(ptrn_list, ax=ax)
_add_hotlines_ticklabels('RP-T-1898-A-3689.datastack', ax, clip_vline=False) 

#plot_cube_slices('RP-T-1898-A-3689.datastack', ax=ax1); 

ax1.plot(x_keVs, y_max, color='r', label='max spectrum') 
ax1.fill_between(x_keVs, y_max, color='r', alpha=0.3)

for y_hot in hotmax_spectra: 
    ax1.plot(x_keVs, y_hot, color=[0.2, 0.1, 0.8], linewidth=0.5) 
#ax1.plot(x_keVs, y_sum, color=[0.3, 1, 0.3], label='sum spectrum')
_add_hotlines_ticklabels('RP-T-1898-A-3689.datastack', ax1) 

ax1.set_xlim([-1, 23])
ax1.set_ylim([-5, 100])
ax1.legend();

ax.set_title('Peak pattern summary')
plt.tight_layout()
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Altogether the spectral data indicates that 11 chemical elements are present in the Susanna drawing: sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), barium (Ba), titanium (Ti), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn) and lead (Pb).

In the next section we will look into the spatial distribution of these elements...

Functions

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get_patterns[source]

get_patterns(elements, tube_keV=30, eoi=None)

Returns sorted pattern dict list, according to alpha peak energy.
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colorize[source]

colorize(elem, eoi=None)

Pick fixed color from nice color map for elements of interest.
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plot_ptrn[source]

plot_ptrn(elem, y, ax, eoi=None, escape=True)

Low level plot element pattern at level `y` in axes `ax`.
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plot_patterns[source]

plot_patterns(ptrn_list, ax=None, eoi=None, escape=True)

Plot overview of element patterns `ptrn_list` in axes `ax`
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plot_puzzle[source]

plot_puzzle(hma, n)

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